The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances (Euclidean, standardized Euclidean, Mahalanobis, city block, cosine and correlation distances) combining four commonly used hierarchical cluster methods (single distance, complete distance, unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling (LDSS) method for constructing different core subsets. The analyses of variance (ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean, standardized Euclidean, Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean, Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages, which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

Clustering is a data mining method that is widely used to group data based on similarity. This clustering process can be used to streamline data so as to facilitate the data ranking process. The purpose of this study was to make comparisons of distance measurements on the K-Means and TOPSIS methods to select students who would take part in industrial visit activities. The method used in this study is the K-Means Algorithm to carry out the clustering process whose results will be processed using the TOPSIS method, both of which use Euclidean, Manhattan and Minkowsky Distance. Based on the clustering process, there were 21 respondents who were eligible to be included, then with TOPSIS a ranking process was carried out. Of the three distance measurements used based on the Pearson Euclidean distance correlation test, the highest results were 0.992, Manhattan 0.982 and Minkowsky 0.980, with ratings one, two and three respectively. For the Spearman correlation, Eculidean is 0.972, Manhattan is 0.982 and Minkowski is 0.955. Thus, Euclidean distance gives the best correlation results, while for alternatives, Manhattan distance or Minkoesky distance can be used.

Semantic similarity, the task of determining whether two sentences convey the same meaning, is central to applications such as paraphrase detection, semantic search, and question answering. Despite the widespread adoption of transformer-based models for this task, their performance is influenced by both the choice of similarity measure and BERT (bert-base-nli-mean-tokens), RoBERTa (all-roberta-large-v1), and MPNet (all-mpnet-base-v2) on the Microsoft Research Paraphrase Corpus (MRPC). Sentence embeddings were compared using cosine similarity, dot product, Manhattan distance, and Euclidean distance, with thresholds optimized for accuracy, balanced accuracy, and F1-score. Results indicate a consistent advantage for MPNet, which achieved the highest accuracy (75.6%), balanced accuracy (71.0%), and F1-score (0.836) when paired with cosine similarity at an optimized threshold of 0.671. BERT and RoBERTa performed competitively but exhibited greater sensitivity to the choice of Similarity metric, with BERT notably underperforming when using cosine similarity compared to Manhattan or Euclidean distance. Optimal thresholds varied widely (0.334–0.867), underscoring the difficulty of establishing a single, generalizable cut-off for paraphrase classification. These findings highlight the value of fine-tuning of both Similarity metrics and thresholds alongside model selection, offering practical guidance for designing high-accuracy semantic similarity systems in real-world NLP applications.

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.

Cultural relics contain unique and rich historical, artistic, economic and other diversified values. However, the imitation and counterfeiting of cultural relics have brought great challenges to the protection and research of cultural relics. In order to improve the problems of strong subjectivity and low precision of the traditional "ophthalmic" identification method, this paper combines spectroscopy with cultural relics to identify the authenticity of pottery and porcelain cultural relics. Under constant temperature and humidity conditions, four kinds of LED with different spectral bands were used as experimental light sources, two Jingdezhen moon flower winter melon porcelain vases with the same appearance and two songxianghu kiln white glazed small clay pots with the same appearance were used as cultural relics samples, and multiple feature points were selected for precision spectral detection. Treat the detection results as multidimensional vectors, calculate the Euclidean distance, Manhattan distance and Chebyshev distance between the vectors, and look for the appropriate threshold to identify the authenticity of the artifact. The results show that: First, the spectral difference between the feature points of cultural relics with the same appearance is significant, and the source of cultural relics can be accurately identified by using the spectral curve; Secondly, this paper calculates the thresholds based on Euclidean distance, Manhattan distance and Chebyshev distance for porcelain and pottery. Among them, the accuracy of identifying porcelain using a threshold based on Euclidean distance is the highest, which is 87.5%; The accuracy of identifying pottery using a threshold based on Manhattan distance was the highest, at 91.67%. By using the spectral curves and thresholds of cultural relics measured by precision spectrometers, this paper generates the spectral identity card of cultural relics. Compared with traditional cultural relics detection, the spectral dimension can better meet the needs of archaeologists for in-depth research on cultural relics.

Data clustering methods have been used extensively for image segmentation in the past decade. In our previous work, we had established that combining the traditional clustering algorithms with a meta-heuristic like Firefly Algorithm improves the stability of the output as well as the speed of convergence. In this paper, we have replaced the Euclidean distance formula with kernels. We have combined Intuitionistic Fuzzy C-Means (IFCM) with Firefly algorithm and replaced Euclidean distance with Gaussian, Hyper tangent and Radial Basis Function Kernels. The intuition behind using Kernels instead of Euclidean or Manhattan distance is explained in this paper. In order to prove that the kernel based counter part of IFCM performs better than its corresponding Euclidean distance based algorithm, we have used standard performance indices such as DB index and Dunn index.

K-means Algorithm is a popular method in cluster analysis, and it is most based on the Euclidean distance. In this paper, a modified version of the K-means algorithm based on the shape similarity distance (SSD-K-means) is presented. The shape similarity distance is one kind of non-metric distance measure for similarity estimation based on the characteristic of differences. To demonstrate the effectiveness of the method we proposed, this new algorithm has been tested on three shape data datasets. Experiment results prove that the performance of the SSD-K-means is better than those of the classical K-means algorithm based on the traditional Euclidean and Manhattan distances.

Problem. The issue of applying A* algorithm for solving pathfinding problems in spatial development of linear objects of land transport infrastructure is considered. Study objective. Determining the most preferred configuration of pathfinding algorithm for solving the problem of spatial development of linear transport infrastructure. Research methods. A* algorithm is widely used for various graph theory applications, including tracing and path planning. The novelty of the work. A number of simple experiments were carried out with the algorithm in order to determine quantitative indicators of its asymptotic complexity, i.e. the number of operations performed and the algorithm time. The set of experiments has a different configuration, determined by the direction of the search (unidirectional and bidirectional), the search metric (Euclidean distance, Manhattan distance, Chebyshev distance) and the method of cell passing (direct and mixed). Research results and conclusions. In general, it can be concluded that bidirectional search requires about 19.17% fewer operations than unidirectional search. Manhattan distance and Chebyshev distance show similar results in terms of path length and execution time. Euclidean distance allows finding a shorter path, but it may take longer to complete.

SummaryPreparatory to the use of volatile oils in systematic studies of Australian rainforest trees, a number of different techniques for handling volatile oil data are reviewed. Compared are two different schemes of character weighting (unweighted and F‐weighted), three different schemes of standardisation (unstandardised, standardisation by range, standardisation by standard deviation), and three different distance measures (Squared Euclidean Distance, Euclidean Distance, Manhattan Metric distance). It is determined empirically that, for volatile oil data, where weighting is both possible and valid, a combination of standardisation and weighting proves most effective; on the other hand, if weighting is not possible, standardisation should not be undertaken. Euclidean Distance and Manhattan Metric distance prove to be the best distance measures.

Instance-based learning techniques use a set of stored training instances to classify new examples. The most common such learning technique is the nearest neighbor method, in which new instances are classified according to the closest training instance. A critical element of any such method is the metric used to determine distance between instances. Euclidean distance is by far the most commonly used metric; no one, however, has systematically considered whether a different metric, such as Manhattan distance, might perform equally well on naturally occurring data sets. Some evidence from psychological research indicates that Manhattan distance might be preferable in some circumstances. This paper examines three different distance metrics and presents experimental comparisons using data from three domains: malignant cancer classification, heart disease diagnosis, and diabetes prediction. The results of these studies indicate that the Manhattan distance metric works works quite well, although not better than the Euclidean metric that has become a standard for machine learning experiments. Because the nearest neighbor technique provides a good benchmark for comparisons with other learning algorithms, the results below include a number of such comparisons, which show that nearest neighbor, using any distance metric, compares quite well to other machine learning techniques.

Social distance detection plays a crucial role in curbing the spread of contagious diseases. In recent years, artificial intelligence (AI) and deep learning (DL) have emerged as a powerful resource for dealing with a wide range of practical challenges and delivering impressively positive outcomes. This article delves into the usage of object recognition, and deep learning to monitor personal and professional interactions between people at a distance. In this paper, it proposes an efficient stacked deep learning technique for accurate and real-time social distance detection (SDD). The proposed method combines the power of object detection and distance estimation between the objects in a stacked manner to improve the accuracy. The proposed technique is evaluated on two popular deep learning models such as MobileNetV2 and InceptionV2. In high-dimensional spaces, Manhattan distance can sometimes be more robust than Euclidean distance. This research work uses Manhattan distance to identify the social distance among the individuals in the crowd. The Manhattan distance with InceptionV2 outperformed well than the MobileNetV2. Experimental results demonstrate the effectiveness of the SDD technique in terms of accuracy and efficiency.

Soil moisture (SM) is a critical parameter in maintaining the balance of water cycle and energy budgets between climate system and the Earth's environment. Generalized regression neural network (GRNN) has been substantially verified as a powerful model for SM estimation due to the ability of capturing complex, non-linear relationships between predictors and responses. However, GRNN builds a full adjacency matrix using Gaussian kernel, which is computationally expensive and may ignore the local structure. In addition, it is laborious to optimize the “spread” parameter. To overcome the above issues, we propose an enhanced generalized regression neural network (EGRNN) for SM estimation, where two main adaptations are made. On the one hand, the City block distance instead of the Euclidean distance is used for building Gaussian kernel. On the other hand, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN) is adopted to yield an empirically sparse adjacency matrix. As the key advantage, the proposed EGRNN weakens the sensitivity to outliers since large differences are weighted more heavily by using Euclidean distance than City block distance. Another advantage is that EGRNN models more local and discriminant information in the pattern layer since only the data points within neighbors are connected by using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN. Experiments conducted in the Qinghai-Tibet Plateau (QTP) demonstrate that; 1) EGRNN outperforms the other four neural network models, with R = 0.9485 and RMSE = 0.0325 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ; 2) It can well capture spatial-temporal dynamics and has higher consistent with the in-situ measurements; 3) It adapts well to different in-situ networks and has better generalization performance.

Effect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizer

Context. Optimization of the method of nearest neighbors k-NN for the classification of text documents by their topics and experimentally solving the problem based on the method.&#x0D; Objective. The study aims to study the method of nearest neighbors k-NN for classifying text documents by their topics. The task of the study is to classify text documents by their topics based on a dataset for the optimal time and with high accuracy.&#x0D; Method. The k-nearest neighbors (k-NN) method is a metric algorithm for automatic object classification or regression. The k-NN algorithm stores all existing data and categorizes the new point based on the distance between the new point and all points in the training set. For this, a certain distance metric, such as Euclidean distance, is used. In the learning process, k-NN stores all the data from the training set, so it belongs to the “lazy” algorithms since learning takes place at the time of classification. The algorithm makes no assumptions about the distribution of data and it is nonparametric. The task of the k-NN algorithm is to assign a certain category to the test document x based on the categories k of the nearest neighbors from the training dataset. The similarity between the test document x and each of the closest neighbors is scored by the category to which the neighbor belongs. If several of k’s closest neighbors belong to the same category, then the similarity score of that category for the test document x is calculated as the sum of the category scores for each of these closest neighbors. After that, the categories are ranked by score, and the test document is assigned to the category with the highest score.&#x0D; Results. The k-NN method for classifying text documents has been successfully implemented. Experiments have been conducted with various methods that affect the efficiency of k-NN, such as the choice of algorithm and metrics. The results of the experiments showed that the use of certain methods can improve the accuracy of classification and the efficiency of the model.&#x0D; Conclusions. Displaying the results on different metrics and algorithms showed that choosing a particular algorithm and metric can have a significant impact on the accuracy of predictions. The application of the ball tree algorithm, as well as the use of different metrics, such as Manhattan or Euclidean distance, can lead to improved results. Using clustering before applying k-NN has been shown to have a positive effect on results and allows for better grouping of data and reduces the impact of noise or misclassified points, which leads to improved accuracy and class distribution.

Article history: Received February 25, 2016 Received in revised format: March 28, 2016 Accepted August 12, 2016 Available online August 12 2016 Multiple attribute decision making (MADM) methods are very useful in choosing the best alternative among the available finite but conflicting alternatives. TOPSIS is one of the MADM methods, which is simple in its methodology and logic. In TOPSIS, Euclidean distances of each alternative from the positive and negative ideal solutions are utilized to find the best alternative. In literature, apart from Euclidean distances, the city block distances have also been tried to find the separations measures. In general, the attribute data are distributed with unequal ranges and also possess moderate to high correlations. Hence, in the present paper, use of statistical distances is proposed in place of Euclidean distances. Procedures to find the best alternatives are developed using statistical and weighted statistical distances respectively. The proposed methods are illustrated with some industrial problems taken from literature. Results show that the proposed methods can be used as new alternatives in MADM for choosing the best solutions. Growing Science Ltd. All rights reserved. 7 © 201